There are a variety of existing methods used for recognizing objects within images. When recognizing objects within images, some methods may use object categorization, which generates categories for the different objects. For example, if categorizing types of animals within images, object categorization may include different categories for lions, bears, zebras, tigers, horses, and geckos. However, there are many problems with the existing methods of object categorization. For example, the existing methods of object categorization try to randomly learn different categories without using any type of learning order. The problem with randomly learning new categories is that it is usually easier to learn new categories based on characteristics from similar categories have already been learned. For another example, the existing methods of object categorization cannot approximate an object from an image unless a category corresponding to that object has already been learned.